Mission is hereby defined as an undertaking of a resource and personnel commitment for achieving a specifically defined goal within specifically defined time period for the benefit of a business organization or a military operation.
Modern information technology infrastructures of business or military operations present a worldwide web of servers, workstations, printers, faxes, data storage devices, routers, switches and other computer and network systems. The main objective of such systems is to improve productivity by streamlining communication process and by delivering strategic applications and data to users within such business or military entity.
Over time, more and more utilizations are being assigned to such infrastructures. Each new utilization requires new methods of assuring communications' and applications' data security and integrity. All such methods, in turn, demand provisioning for more hardware, software and manpower resources for their upkeep and maintenance.
As a result, in most of today's global information technology infrastructures, there exists a significant disconnect between the high-level information about the critical missions at all phases of business and military operations and knowledge of IT infrastructure and application—data relationships that these missions depend on.
Consequently, the IT resources are either over provisioned, under provisioned, or both. This results in overspending and can lead to mission failures. Moreover, such over provisioning is not always possible.
Therefore, there exists a need to provide a method for connecting the knowledge about missions with information about corresponding IT resources. More specifically, there exist a need for integrating existing high level mission application data with metadata produced by systems and applications within IT infrastructure.
Modern applications follow a layered architecture using application and data middleware tiers. Such layering typically reflects different levels of application abstraction such as user interface, business logic, application services, data services and group communication services, as well as infrastructure (node) virtualization.
Several approaches are possible to collect information about the cross-layer data relations. For instance, there is a known data relations modeling method that allows discovering end-to-end application and data relations. This method is implemented by using provenance—aware storage systems, that are capable of collecting complete history of information that describes data in sufficient detail to facilitate reproduction and to enable validation of results, and by using systems that attempt to dynamically track the data transfers and modifications.
One example of such data relations modeling system is Galapagos system, which is designed to discover usage of data in a large distributed system. In essence, this system enriches basic infrastructure discovery with knowledge of how data is used by applications (e.g., business objects, tables, files, etc.) in addition to information about data providers (e.g., enterprise information system, database systems, etc.)
Galapagos discovers and represents all end-to-end, multi-tier dependencies between applications and data in an n-tiered distribution system. Moreover, it does so in an easily extensible fashion: adding a new (n+ith) middleware tier in an n-tiered system automatically includes the new tier in its representation of end-to-end relationships. This system is described in great detail in Integrated Network Management, 2007. IM '07 10th IFIP/IEEE International Symposium. K. Magoutis, M. Devarakonda, K. Muniswamy-Reddy and IBM T.J. Watson Research Center, “Galapagos: Automatically Discovering Application—Data Relationships in Networked Systems” the whole contents of which is incorporated by reference as if fully set forth herein.
Representation of mission level data values and their exchanges is best described by a Value Network Analysis (VNA) that usually relies on Community Knowledge Systems (CKS) and information mining technologies. VNA is a business modeling methodology for understanding internal and external value networks. Technically, a value network can be represented as a direct graph where the nodes represent network participants and the edges show the flow of material objects and non-material (intangible) values such as information or brand recognition values in the network. Generally, VNA is used to quantitatively analyze the flow of tangible and intangible assets in business networks.
While VNA is used to discover hidden relationships between tangible and intangible flows in the enterprises, Community Knowledge Systems (CKS) and information mining system are used to create Value Network topologies and related value exchange processes.
It would be highly desirable to provide a technique for connecting the knowledge about missions discovered using VNA and corresponding data relations modeling systems (Galapagos and provenance-aware systems) to create a method and a system that would allow for optimization of provisioning for IT resources, and for prioritizing of control for data and application security and integrity.